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The authors introduce STONE, a large-scale multi-modal dataset for off-road robot navigation featuring trajectory-guided 3D traversability maps. These maps are automatically generated using a novel annotation-free pipeline that leverages LiDAR scans and geometric attributes to assign traversability labels. The dataset includes synchronized data from LiDAR, RGB cameras, and 4D imaging radars across diverse off-road environments and conditions, enabling research into robust 3D traversability prediction.
Forget manual labeling: STONE offers a massive, automatically-labeled dataset for off-road robot navigation, unlocking scalable training for robust 3D traversability prediction.
Reliable off-road navigation requires accurate estimation of traversable regions and robust perception under diverse terrain and sensing conditions. However, existing datasets lack both scalability and multi-modality, which limits progress in 3D traversability prediction. In this work, we introduce STONE, a large-scale multi-modal dataset for off-road navigation. STONE provides (1) trajectory-guided 3D traversability maps generated by a fully automated, annotation-free pipeline, and (2) comprehensive surround-view sensing with synchronized 128-channel LiDAR, six RGB cameras, and three 4D imaging radars. The dataset covers a wide range of environments and conditions, including day and night, grasslands, farmlands, construction sites, and lakes. Our auto-labeling pipeline reconstructs dense terrain surfaces from LiDAR scans, extracts geometric attributes such as slope, elevation, and roughness, and assigns traversability labels beyond the robot's trajectory using a Mahalanobis-distance-based criterion. This design enables scalable, geometry-aware ground-truth construction without manual annotation. Finally, we establish a benchmark for voxel-level 3D traversability prediction and provide strong baselines under both single-modal and multi-modal settings. STONE is available at: https://konyul.github.io/STONE-dataset/
